Distributed application of guideline-based decision support through mobile devices: Implementation and evaluation

Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach

BACKGROUND

With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid demand for intelligent systems that can recommend comprehensive long-term care plans for older adults with disabilities who have stable clinical conditions.

OBJECTIVE

This study aims to utilize deep learning methods to recommend comprehensive care plans for the older adults with disabilities.

METHODS

We model the care data of older adults with disabilities using a bipartite graph. Additionally, we employ a prediction-based graph self-supervised learning (SSL) method to mine deep representations of graph nodes. Furthermore, we propose a novel graph Transformer architecture that incorporates eigenvector centrality to augment node features and uses graph structural information as references for the self-attention mechanism. Ultimately, we present the Bipartite Graph Transformer (BiT) model to provide personalized long-term care plan recommendation.

RESULTS

We constructed a bipartite graph comprising of 1917 nodes and 195 240 edges derived from real-world care data. The proposed model demonstrates outstanding performance, achieving an overall F1 score of 0.905 for care plan recommendations. Each care service item reached an average F1 score of 0.897, indicating that the BiT model is capable of accurately selecting services and effectively balancing the trade-off between incorrect and missed selections.

DISCUSSION

The BiT model proposed in this paper demonstrates strong potential for improving long-term care plan recommendations by leveraging bipartite graph modeling and graph SSL. This approach addresses the challenges of manual care planning, such as inefficiency, bias, and errors, by offering personalized and data-driven recommendations. While the model excels in common care items, its performance on rare or complex services could be enhanced with further refinement. These findings highlight the model's ability to provide scalable, AI-driven solutions to optimize care planning, though future research should explore its applicability across diverse healthcare settings and service types.

CONCLUSIONS

Compared to previous research, the novel model proposed in this article effectively learns latent topology in bipartite graphs and achieves superior recommendation performance. Our study demonstrates the applicability of SSL and graph transformers in recommending long-term care plans for older adults with disabilities.

Patterns of Comorbidity, Disability, and Home Health Care Service for Older Adults With Physical Disability: A Latent Class Analysis and Visualized Illustration

Understanding the relationships between comorbidity, disability, and home health-care services aids in user-centered care design. This study identifies patterns of these factors among older adults with physical disability living at home and explores their associations. This cross-sectional study included community-dwelling older adults assessed for Long-term Care Insurance from September 1 to December 31, 2018, in Yiwu, Zhejiang, China. We analyzed deidentified data on sociodemographics, physical disability, chronic conditions, and care plans for 1882 participants using latent class analysis and multinomial logistic regression. Five comorbidity patterns (other sporadic diseases, cancer comorbidity, other sporadic comorbidities, coronary atherosclerotic heart disease, and fracture) and four home health-care service patterns (assistance & facilitation of elimination, bedridden assistance & stoma care, bedside assistance & stoma care, and assistance & tube feeding) were identified. Four disability patterns emerged (bedridden without incontinence, bedridden & with upper limb mobility, off-bed & ADLs limited, and bedridden with incontinence). Complex associations between these patterns were observed. The study highlights the heterogeneity and complexity of the above patterns, emphasizing the importance of person-centered home health-care services and technology-supported care strategies.

The Case Manager: An Agent Controlling the Activation of Knowledge Sources in a FHIR-Based Distributed Reasoning Environment

BACKGROUND

Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines.

OBJECTIVES

As in many multi-agent systems we needed to coordinate the activities of all agents involved. Moreover, since the agents share a common blackboard where all patients' data are stored, we also needed to implement a mechanism for the prompt notification of each agent upon addition of new information potentially triggering its activation.

METHODS

The communication needs have been investigated and modeled using the HL7-FHIR (Health Level 7-Fast Healthcare Interoperability Resources) standard to ensure proper semantic interoperability among agents. Then a syntax rooted in the FHIR search framework has been defined for representing the conditions to be monitored on the system blackboard for activating each agent.

RESULTS

The Case Manager (CM) has been implemented as a dedicated component playing the role of an orchestrator directing the behavior of all agents involved. Agents dynamically inform the CM about the conditions to be monitored on the blackboard, using the syntax we developed. The CM then notifies each agent whenever any condition of interest occurs. The functionalities of the CM and other actors have been validated using simulated scenarios mimicking the ones that will be faced during pilot studies and in production.

CONCLUSION

The CM proved to be a key facilitator for properly achieving the required behavior of our multi-agent system. The proposed architecture may also be leveraged in many clinical contexts for integrating separate legacy services, turning them into a consistent telemedicine framework and enabling application reusability.